Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 30, 2025SymmetryOpen Access

Application of an Improved Double Q-Learning Algorithm in Ground Mobile Robots

View Full Paper
Ask AI
Bookmark
Share

Authors

JZJinchao ZhaoZhejiang UniversityYZYa ZhangWuhan University of TechnologyNWNan WuNingbo No. 2 Hospital

Discussion

Loading...

Member takes

Implication

A novel double Q-learning approach enhances path planning and exploration in autonomous robots, indicating improved performance.

Key Points

  • The improved algorithm reduces navigation risks in complex environments by optimizing Q value calculations.
  • Utilization of priority experience replay enhances the data extraction effectiveness from the experience pool.
  • In performance comparisons, the proposed algorithm outperforms traditional methods by significant margins.
  • The algorithm's design allows unmanned robots to independently identify the shortest path to their destination.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e308a7d58c25ebb13abhttps://doi.org/10.3390/sym17091530
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments2024 · 2 citations
  2. 2Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning2024 · 4 citations
  3. 3Cautious Model Predictive Control Using Gaussian Process Regression2019 · 565 citations
  4. 4Impedance Learning-Based Adaptive Control for Human–Robot Interaction2021 · 85 citations